Foam reduces gas mobility in porous media by trapping substantial amount of gas and applying a viscous resistance of flowing lamellas to gas flow. In mechanistic foam modeling, gas relative permeability is significantly modified by gas trapping, while an effective gas viscosity, which is a function of flowing lamella density, is assigned to flowing gas. A complete understanding of foam mobility in porous media requires being able to predict the effects of pressure gradient, foam texture, rock and fluid properties on gas trapping, and therefore gas relative permeability, and effective gas viscosity. In the foam literature, separating the contributions of gas trapping and effective gas viscosity on foam mobility has not been achieved because the dynamics of gas trapping and its effects on the effective gas viscosity have been neglected. In this study, dynamics of foam mobility in porous media is investigated with a special focus on gas trapping and its effects on gas relative permeability and effective gas viscosity. Three-dimensional pore-network models representative of real porous media coupled with fluid models characterizing a lamella flow through a pore throat are used to predict flow paths, threshold pressure gradient and Darcy velocity of foam. It is found that the threshold path and the pore volume open above the threshold pressure are independent of the fluid model used in this study. Furthermore, analytical correlations of flowing gas fraction as functions of pressure gradient, lamella density, rock and fluid properties are obtained. At a constant pressure gradient, flowing gas fraction increases as overall lamella density decreases. In the discontinuous-gas foam flow regime, there exists a threshold pressure gradient, which increases with overall lamella density. One of the important findings of this study is that gas relative permeability is a strong non-linear function of flowing gas fraction, opposing most of the existing theoretical models. However, the shape of the relative gas permeability curve is poorly sensitive to overall lamella density. Flowing and trapped lamella densities change with pressure gradient. Moreover, analytical correlations of effective gas viscosity as functions of capillary number, lamella density and rock properties are obtained by up-scaling a commonly used pore-scale apparent gas (lamella) viscosity model. Effective gas viscosity increases nonlinearly with flowing lamella density, which opposes to the existing linear foam viscosity models. In addition, the individual contributions of gas trapping and effective gas viscosity on foam mobility are quantified for the first time. The functional relationship between effective gas viscosity and flowing lamella density in the presence of dynamic trapped gas is verified. A mechanistic foam model is developed by using the analytical correlations of flowing gas fraction and effective gas viscosity generated from the pore-network study and a modified population balance model. The developed model is successful in simulating unsteady-state and steady state flow of foam through porous media. Moreover, the flow behaviors in high- and low-quality flow regimes are verified by the experimental studies in the literature. Finally, the simulation results are successfully history matched with two different core-flood data. / text

Genetic regulatory networks can model dynamics of cells. They also allow for studying the effect of internal or external interventions. Selectively applying interventions towards a certain objective is known as controlling network dynamics. In this thesis work, the issue of how the external interventions af fect the network is studied. The effects are determined using differential gene expression analysis. The differential gene expression problem is further studied to improve the power of the given method. Control problem for dynamic discrete regulatory networks is formulated. This also addresses the needs for various control strategies, e.g., finite horizon, infinite horizon, and various accounting of state and intervention costs. Control schemes for small to large networks are proposed and experimented. A case study is provided to show how the proposals are exploited / also given is the need for and effectiveness of various control schemes.

Este estudo tem como objetivo analisar os trade-offs existentes entre os custos logísticos, os incentivos fiscais baseados no ICMS e o custo da neutralização das emissões de carbono geradas nos problemas de localização de Fábricas e Centros de Distribuição. Para isso, elaborou-se um modelo de programação linear inteira mista (PLIM) em GAMS, capaz de determinar o menor custo total de um problema, através da otimização de sua função objetivo composta pelos custos fixos e variáveis dos centros de distribuição e fábricas, custos de transporte (frete de transferência e distribuição), benefícios fiscais e custos ambientais. O modelo foi elaborado de modo a possuir flexibilidade suficiente para simular os diversos cenários que se fizeram necessários durante as análises. Utilizando-se deste modelo, foram avaliados diversos cenários com base em dados reais de uma empresa de bens de consumo não duráveis. Alguns desses cenários estudados mostraram algumas distorções causadas pela existência de incentivos fiscais em alguns Estados brasileiros, mostrando como a guerra fiscal no País pode influenciar decisões estratégicas de negócio. A partir dos resultados obtidos, concluiu-se que o benefício fiscal associado ao crédito presumido de ICMS tem impacto significativo nas decisões de localização, reduzindo de forma relevante os custos totais. Já os custos ambientais, relacionados a neutralização das emissões de carbono, apesar de serem importantes nas decisões de empresas social e ambientalmente responsáveis, possuem peso econômico desprezível e não alteram o resultado da análise. Isso mostra que a política fiscal brasileira gera um aumento da emissão de poluentes na atmosfera e um aumento do desgaste e do fluxo de veículos de transporte pelas rodovias do País. / The main object of this work piece is to analyze existing trade-offs among logistic costs, tax incentives based on ICMS and carbon emission volume variations, to be able to define how these factors influence the network localization of Plants and Distribution Centers. To achieve this objective, a Mixed Integer Linear Programming model was developed in GAMS. The model is able to determine the minimum total cost for a given problem through the optimization of a specific objective function. The components of the objective function are: storage costs, transportation costs (transference and distribution freights), operational fixed costs and tax incentives. The model was designed to have enough flexibility to simulate multiple scenarios required to carry out the analysis. Several logistics configurations were examined using this model. All of the scenarios were established based on real data provided by a consumer goods industry. Nevertheless, some of the studied network configurations are distortions caused by existing tax incentives in some Brazilian states, showing how the fiscal war can influence strategic business decisions. Based on the results, one concludes that the tax benefits associated to the ICMS discounts applied in some Brazilian states actually have significant impact in the location decisions because it cuts down a relevant portion of the operational costs, whereas the carbon credits do not change the chosen network configuration, once it has shown a limited potential for financial benefit. The carbon emissions reduction is, in the other hand, an important aspect of the decisions making in social and environmental responsible companies as it can modify the image of the institution and the way it is perceived by the market.

Accurate prediction of colloid deposition rates in porous media is essential in several applications. These include natural filtration of pathogenic microorganisms such as bacteria, viruses, and protozoa, transport and fate of colloid-associated transport of contaminants, deep bed and river bank filtration for water treatment, fate and transport of engineered nanoparticles released into the environment, and bioremediation of contaminated sites. Colloid transport in porous media is a multi-scale problem, with length scales spanning from the sub-pore scale, where the particle-soil interaction forces control the deposition, up to the Darcy scale, where the macroscopic equations governing particle transport are formulated. Colloid retention at the Darcy scale is due to the lumped effect of processes occurring at the pore scale. This requires the incorporation of the micro-scale physics into macroscopic models for a better understanding of colloid deposition in porous media. That can be achieved through pore-scale modeling and the subsequent upscaling to the Darcy scale. Colloid Filtration Theory (CFT), the most commonly used approach to describe colloid attachment onto the soil grains in the subsurface, is found to accurately predict the deposition rates of micron-sized particles under favorable conditions for deposition. But, CFT has been found to over predict particle deposition rates at low flow velocity conditions, typical of groundwater flow, and for nanoscale particles. Also, CFT is found to be inapplicable at typical environmental conditions, where conditions become unfavorable for deposition, due to factors not considered in CFT such as deposition in the secondary minimum of the interaction energy profile, grain surface roughness, surface charge heterogeneity of grains and colloids, and deposition at grain-to-grain contacts. To the best of our knowledge, mechanistic-based models for predicting colloid deposition rates under unfavorable conditions do not exist. Currently, fitting the colloid breakthrough curve (BTC), obtained from the laboratory column-or field-scale experiments, to the advection-dispersion-deposition model is used to estimate the values of deposition rate coefficients. Because of their small size (less than 100 nm), nanoparticles, a sub-class of colloids, may interact with the porous medium in a different way as compared to the larger colloids, resulting in different retention mechanisms for nanoparticles and micron-sized particles. This emphasizes the need to study nanoparticles separately from larger, micrometer-sized colloids to better understand nanoparticle retention mechanisms. The work reported in this thesis contributes towards developing mathematical models to predict nanoparticle movement in porous media. A comprehensive mechanistic approach is employed by integrating pore-scale processes into Darcy-scale models through pore-network modeling to upscale nanoparticle transport in saturated porous media to the Darcy scale, and to develop correlation equations for the Darcy-scale deposition parameters in terms of various measurable parameters at Darcy scale. Further, a one-dimensional mathematical model to simulate the co-transport of viruses and colloids in partially saturated porous media is developed to understand the relative importance of various interactions on virus transport in porous media. Pore-network modeling offers a valuable upscaling tool to express the macroscopic behavior by accounting for the relevant physics at the underlying pore scale. This is done by idealizing the pore space as an interconnected network of pore elements of different sizes and variably connected to each other, and simulating flow and transport through the network of pores, with the relevant physics implemented on a pore to pore basis (Raoof, 2011). By comparing the results of pore-network modeling with an appropriate mathematical model describing the macro-scale behavior, a relationship between the properties at the macro scale and those at the pore scale can be obtained. A three dimensional multi-directional pore-network model, PoreFlow, developed by Raoof et al. (2010, 2013) is employed in this thesis, which represents the porous medium as an interconnected network of cylindrical pore throats and spherical pore bodies, to upscale nanoparticle transport from pore scale to the Darcy scale. The first step in this procedure is to obtain relationships between adsorbed mass and aqueous mass for a single pore. A mathematical model is developed to simulate nanoparticle transport in a saturated cylindrical pore by solving the full transport equation, considering various processes such as advection, diffusion, hydrodynamic wall effects, and nanoparticle-collector surface interactions. The pore space is divided into three different regions: bulk, diffusion and potential regions, based on the dominant processes acting in each of these regions. In both bulk and diffusion regions, nanoparticle transport is governed by advection and diffusion. However, in the diffusion region, the diffusion is significantly reduced due to hydrodynamic wall effects. Nanoparticle-collector interaction forces dominate the transport in the potential region where deposition occurs. A sensitivity analysis of the model indicates that nanoparticle transport and deposition in a pore is significantly affected by various pore-scale parameters such as the nanoparticle and collector surface potentials, ionic strength of the solution, flow velocity, pore radius, and nanoparticle radius. The model is found to be more sensitive to all parameters under favorable conditions. It is found that the secondary minimum plays an important role in the deposition of small as well as large nanoparticles, and its contribution is found to increase as the favorability of the surface for adsorption decreases. Correlation equations for average deposition rate coefficients of nanoparticles in a saturated cylindrical pore under unfavorable conditions are developed as a function of nine pore-scale parameters: the pore radius, nanoparticle radius, mean flow velocity, solution ionic strength, viscosity, temperature, solution dielectric constant, and nanoparticle and collector surface potentials. Advection-diffusion equations for nanoparticle transport are prescribed for the bulk and diffusion regions, while the interaction between the diffusion and potential regions is included as a boundary condition. This interaction is modeled as a first-order reversible kinetic adsorption. The expressions for the mass transfer rate coefficients between the diffusion and the potential regions are derived in terms of the interaction energy profile between the nanoparticle and the collector. The resulting equations are solved numerically for a range of values of pore-scale parameters. The nanoparticle concentration profile obtained for the cylindrical pore is averaged over a moving averaging volume within the pore in order to get the 1-D concentration field. The latter is fitted to the 1-D advection-dispersion equation with an equilibrium or kinetic adsorption model to determine the values of the average deposition rate coefficients. Pore-scale simulations are performed for three values of Péclet number, Pe = 0.05, 5 and 50. It is found that under unfavorable conditions, the nanoparticle deposition at pore scale is best described by an equilibrium model at low Péclet numbers (Pe = 0.05), and by a kinetic model at high Péclet numbers (Pe = 50). But, at an intermediate Pe (e.g., near Pe = 5), both equilibrium and kinetic models fit the 1-D concentration field. Correlation equations for the pore-averaged nanoparticle deposition rate coefficients under unfavorable conditions are derived by performing a multiple-linear regression analysis between the estimated deposition rate coefficients for a single pore and various pore-scale parameters. The correlation equations, which follow a power law relationship with nine pore-scale parameters, are found to be consistent with the column-scale and pore-scale experimental results, and qualitatively agree with CFT. Nanoparticle transport is upscaled from pore to the Darcy scale in saturated porous media by incorporating the correlations equations for the pore-averaged deposition rate coefficients of nanoparticles in a cylindrical pore into a multi-directional pore-network model, PoreFlow (Raoof et al., 2013). Pore-network model simulations are performed for a range of parameter values, and nanoparticle BTCs are obtained from the pore-network model. Those curves are then modeled using 1-D advection-dispersion equation with a two-site first-order reversible deposition, with terms accounting for both equilibrium and kinetic sorption. Kinetic sorption is found to become important as the favorability of the surface for deposition decreases. Correlation equations for the Darcy¬scale deposition rate coefficients under unfavorable conditions are developed as a function of various measurable Darcy-scale parameters, including: porosity, mean pore throat radius, mean pore water velocity, nanoparticle radius, ionic strength, dielectric constant, viscosity, temperature, and surface potentials on the nanoparticle and grain surface. The correlation equations are found to be consistent with the observed trends from the column experiments available in the literature, and are in agreement with CFT for all parameters, except for the mean pore water velocity and nanoparticle radius. The Darcy-scale correlation equations contain multipliers whose values for a given set of experimental conditions need to be determined by comparing the values of the deposition rate coefficients predicted by the correlation equations against the estimated values of Darcy-scale deposition parameters obtained by fitting the BTCs from column or field experiments with 1-D advection-dispersion-deposition model. They account for the effect of factors not considered in this study, such as the physical and chemical heterogeneity of the grain surface and nanoparticles, flow stagnation points, grain-to-grain contacts, etc. Colloids are abundant in the subsurface and have been observed to interact with a variety of contaminants, including viruses, thereby significantly influencing their transport. A mathematical model is developed to simulate the co-transport of viruses and colloids in partially saturated porous media under steady state flow conditions. The virus attachment to the mobile and immobile colloids is described using a linear reversible kinetic model. It is assumed that colloid transport is not affected by the presence of attached viruses on its surface, and hence, colloid transport is decoupled from virus transport. The governing equations are solved numerically using an alternating three-step operator splitting approach. The model is verified by fitting three sets of experimental data published in the literature: (1) Syngouna and Chrysikopoulos (2013) and (2) Walshe et al. (2010), both on the co-transport of viruses and clay colloids under saturated conditions, and (3) Syngouna and Chrysikopoulos (2015) for the co-transport of viruses and clay colloids under unsaturated conditions. The model results are found to be in good agreement with the observed BTCs under both saturated and unsaturated conditions. Then, the developed model was used to simulate the co-transport of viruses and colloids in porous media under unsaturated conditions, with the aim of understanding the relative importance of various processes on the co-transport of viruses and colloids. The virus retention in porous media in the presence of colloids is greater under unsaturated conditions as compared to the saturated conditions due to: (1) virus attachment to the air-water interface (AWI), and (2) co-deposition of colloids with attached viruses on its surface to the AWI. A sensitivity analysis of the model to various parameters showed that virus attachment to AWI is the most sensitive parameter affecting the BTCs of both free viruses and total mobile viruses, and has a significant effect on all parts of the BTC. The free and the total mobile virus BTCs are mainly influenced by parameters describing virus attachment to the AWI, virus interactions with mobile and immobile colloids, virus attachment to solid-water interface (SWI), and colloid interactions with SWI and AWI. The virus BTC is relatively insensitive to parameters describing the maximum adsorption capacity of the AWI for colloids, inlet colloid concentration, virus detachment rate coefficient from the SWI, maximum adsorption capacity of the AWI for viruses, and inlet virus concentration.

Cognitive radio (CR) networks and heterogeneous cellular networks are promising approaches to satisfy the demand for higher data rates and better connectivity. A CR network increases the utilization of the radio spectrum by opportunistically using it. Heterogeneous networks provide high data rates and improved connectivity by spatially reusing the spectrum and by bringing the network closer to the user. Interference presents a critical challenge for reliable communication in these networks. Accurately modeling it is essential in ensuring a successful design and deployment of these networks. We first propose modeling the aggregate interference power at a primary receiver (PU-Rx) caused from transmissions by randomly located cognitive users (CUs) in a CR network as a shifted lognormal random process. Its parameters are determined using a moment matching method. Extensive benchmarking shows that the proposed model is more accurate than the lognormal and Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, interweave and underlay modes of CR operation, and path-loss, time-correlated shad-owing and fading of the various links in the network. It leads to new expressions for the probability distribution function, level crossing rate (LCR), and average exceedance duration (AED). The impact of cooperative spectrum sensing is also characterized. We also apply and validate the proposed model by using it to redesign the primary exclusive zone to account for the time-varying nature of interference. Next we model the uplink inter-cell aggregate interference power in homogeneous and heterogeneous cellular systems as a simpler lognormal random variable. We develop a new moment generating function (MGF) matching method to determine the lognormal’s parameters. Our model accounts for the transmit power control, peak transmit power constraint, small scale fading and large scale shadowing, and randomness in the number of interfering mobile stations and their locations. In heterogeneous net-works, the random nature of the number and locations of low power base stations is also accounted for. The accuracy of the proposed model is verified for both small and large values of interference. While not perfect, it is more accurate than the conventional Gaussian and moment-matching-based lognormal and Gamma distribution models. It is also performs better than the symmetric-truncated stable and stable distribution models, except at higher user density.

This term project deals with the functionality of Spanning Tree protocols, especially the Rapid Spanning Tree Protocol, and the Multiple Spanning Tree Protocol. The primary usage of spanning tree protocols is the prevention of loops within the data link layer, the prevention of a broadcast storm, and also dealing with redundancy in the network. Moreover, the project contains the description of configuration of these protocols on Cisco devices. The main goal of this thesis is to implement the Multiple Spanning Tree protocol into INET framework within the OMNeT++ simulation system. Then, the implemented solution is tested and it's functionality is compared with the referential behavior in a Cisco network.

No description available.

The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.

Approved for public release, distribution is unlimited / Wireless mesh networking has emerged as the successor of the traditional ad hoc networks. New technological advances, the standardization of protocols and interfaces and the maturity of key components have made it possible for current mesh research groups to set goals that are really close to the world's expectations. The objective of this research is to design and implement a simulation toolkit for wireless mesh clusters that can be used as an additional performance evaluation technique for the Tactical Network Topology program of Naval Postgraduate School. This toolkit is implemented in the OPNET simulation environment and it incorporates various nodes running different ad hoc routing protocols. Furthermore, the investigation of a suitable combination of protocols for the Tactical Network Topology is achieved by creating scenarios and running a number of simulations using the mesh toolkit. / Captain, Hellenic Air Force

Flow and transport phenomena in the subsurface often span a wide range of length (nanometers to kilometers) and time (nanoseconds to years) scales, and frequently arise in applications of CO₂ sequestration, pollutant transport, and near-well acid stimulation. Reliable field-scale predictions depend on our predictive capacity at each individual scale as well as our ability to accurately propagate information across scales. Pore-scale modeling (coupled with experiments) has assumed an important role in improving our fundamental understanding at the small scale, and is frequently used to inform/guide modeling efforts at larger scales. Among the various methods, there often exists a trade-off between computational efficiency/simplicity and accuracy. While high-resolution methods are very accurate, they are computationally limited to relatively small domains. Since macroscopic properties of a porous medium are statistically representative only when sample sizes are sufficiently large, simple and efficient pore-scale methods are more attractive. In this work, two Eulerian pore-network models for simulating single-phase flow and solute transport are developed. The models focus on capturing two key pore-level mechanisms: a) partial mixing within pores (large void volumes), and b) shear dispersion within throats (narrow constrictions connecting the pores), which are shown to have a substantial impact on transverse and longitudinal dispersion coefficients at the macro scale. The models are verified with high-resolution pore-scale methods and validated against micromodel experiments as well as experimental data from the literature. Studies regarding the significance of different pore-level mixing assumptions (perfect mixing vs. partial mixing) in disordered media, as well as the predictive capacity of network modeling as a whole for ordered media are conducted. A mortar domain decomposition framework is additionally developed, under which efficient and accurate simulations on even larger and highly heterogeneous pore-scale domains are feasible. The mortar methods are verified and parallel scalability is demonstrated. It is shown that they can be used as “hybrid” methods for coupling localized pore-scale inclusions to a surrounding continuum (when insufficient scale separation exists). The framework further permits multi-model simulations within the same computational domain. An application of the methods studying “emergent” behavior during calcite precipitation in the context of geologic CO₂ sequestration is provided. / text